The present disclosure relates generally to plant asset management systems and, more particularly, to a method and apparatus for machine state quantification in machinery management systems.
Early notification of malfunctions or other alarm conditions in a machine or process assists operators in reacting to and curing the malfunction or condition. Such early warning of malfunctions is particularly important for machines and processes used in, for example, nuclear power plants, oil refineries, conventional power plants, pipeline pumping stations, manufacturing facilities, aircraft engines and in other critical facilities and applications. Early detection of a malfunction may allow an operator to prevent extensive damage to machines, stop a potentially dangerous condition, and maintain efficient and continuous operation.
Sensors that monitor various machine conditions (e.g., vibration, temperature, flow pressure, lubrication flow and power output and/or demand) are well known. Based on the data from the sensors, machine controllers determine whether a malfunction has occurred or if conditions in the machine are ripe for a malfunction. The controllers may also apply threshold levels to the sensor data to determine if the machine condition exceeds a desired level. If the threshold level is exceeded by the sensor data, then the controller may generate an alarm condition. However, if the threshold level is set too high, the machine may have already malfunctioned and be damaged by the time the controller issues an alarm. On the other hand, if the threshold level is too low, the controller may issue too many false alarms.
In addition, Plant Asset Management (PAM) software is also used to provide high-level notifications of possible machinery problems. As a practical matter, such warnings should be credible or the operators/users of the system will not pay attention to the warnings. It is common for machinery operators to get in the habit of acknowledging warning messages without follow up when the messages frequently turn out to be invalid. Thus, reducing the software's propensity to “cry wolf” is an important feature for a valuable product.
Certain turbine control systems (e.g., Mark V, Mark VI by General Electric) implement logic that controls whether a machine is shut down based on vibration. This logic sometimes has “permissives” embedded therein that qualify an algorithm output based on time at running speed, and whether the unit is within an acceptance window for generator output (i.e., load). This permissive strategy is invisible to the customer, as is nearly all internal functions of the control system. It would be desirable to be able to improve the quality of automated machinery management by applying additional criteria to deterministic rules that reduce the number of incorrect machine malfunction notifications. It is further desirable for the deterministic rules to be able to handle criteria for steady state load by evaluating past changes, rather than being guided by a simple acceptance range. A steady state operating condition is significant, since machine vibration or other parameters can vary while the machine settles into a given operating mode. This settling can be a normal phenomenon not warranting an alarm condition.